机器学习
47 篇 · 资讯流GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives
Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tin
原文 ↗GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed
Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal
原文 ↗Characterizing High Bandwidth Flash for LLM Serving
Large language model (LLM) serving requires substantial memory to store model weights and KV caches. As models grow larg
原文 ↗$S^3$: Spectral Null-Space Swap Makes Reasoning Models Efficient
LLMs trained with Chain-of-thought excel in reasoning capability, but often come with excessive token cost. We find that
原文 ↗Rethinking Causal Action Tokenization with Conditional Annealing in Flow Matching
Autoregressive Vision-Language-Action (VLA) models offer a scalable path to robot learning, yet existing action tokenize
原文 ↗SOLO: Pretraining Billion-Parameter Language Models with Shared-Output Local Learning
Large language models are trained with backpropagation, whose global gradient coordinates all layers but forces each to
原文 ↗Jailbreaks for Black-Box Uncertainty Quantification in Large Reasoning Models
While Large Reasoning Models (LRMs) excel at complex reasoning, alignment through reinforcement learning often induces s
原文 ↗TempoKV: Timely Staging of LLM KV Caches for Memory-Semantic Flash
Reusable prefix key-value (KV) caches can outgrow GPU memory in large language model (LLM) serving. A memory-semantic fl
原文 ↗QwenGyre: An Elastic Reinforcement Learning Framework for Training xLong-Horizon Agents
Large language model (LLM) agents increasingly undertake extreme-long (xlong) horizon tasks, where a single execution ca
原文 ↗GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI
Large Language Models (LLMs) typically exhibit a performance profile where reliability degrades as task complexity incre
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